There is no shortage of package managers. Each tool makes its own set of tradeoffs regarding speed, ease of use, customizability, and reproducibility. Guix occupies a sweet spot, providing reproducibility by design as pioneered by Nix, package customization à la Spack from the command line, the ability to create container images without hassle, and more.
Beyond the “feature matrix” of the tools themselves, a topic that is often overlooked is packages—or rather, what’s inside of them. Chances are that a given package may be installed using any of the many tools at your disposal. But are you really getting the same thing regardless of the tool you are using? The answer is “no”, contrary to what one might think. The author realized this very acutely while fearlessly attempting to package the PyTorch machine learning framework for Guix.
This post is about the journey packaging PyTorch the Guix way, the rationale, a glimpse at what other PyTorch packages out there look like, and conclusions we can draw for high-performance computing and scientific workflows.
